In this paper, we provide, for the first time, a detailed analysis of the CI problem within the FSL framework. Our results show that even small CI levels can introduce a significant performance drop for all the methods considered. Moreover, we find that only a few models benefit from Random-Shot meta-training [1]}, [2]}, [3]} over the classical (balanced) episodic meta-training [4]}; while pairing the meta-training procedures with Random Over-Sampling offers a substantial advantage.
The experimental results show that imbalance severity at the dataset level depends on the size of the dataset.
Our contributions can be summarized as follows:
